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/stats

Displays memory usage statistics for the current session and project including counts by category, age distribution, and API latency. Use when checking how many memories exist, reviewing session activity, or auditing memory distribution across categories.

From plugin
mem0
63k32 skills1 MCP
Install
$ npx -y skills add mem0ai/mem0 --skill stats --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/stats

Context preview

The summary Claude sees to decide when to auto-load this skill.

Displays memory usage statistics for the current session and project including counts by category, age distribution, and API latency. Use when checking how many memories exist, reviewing session activity, or auditing memory distribution across categories.

SKILL.md

stats.SKILL.md
name: stats
description: Displays memory usage statistics for the current session and project including counts by category, age distribution, and API latency. Use when checking how many memories exist, reviewing session activity, or auditing memory distribution across categories.

Mem0 Stats

Show session and lifetime memory statistics.

Execution

Step 1: Gather session stats

Run the session stats reporter:

SCRIPT_DIR="${CLAUDE_PLUGIN_ROOT:-${CODEX_PLUGIN_ROOT:-${CURSOR_PLUGIN_ROOT:-}}}/scripts"
python3 "$SCRIPT_DIR/session_stats.py" peek 2>/dev/null || echo "{}"

The `peek` command returns JSON without clearing the stats file (unlike `report`).

If the script returns empty or errors, note "No session data available" and continue.

Step 2: Fetch lifetime and session stats from API

**Lifetime stats:** Call `get_memories` to fetch all memories for this project:

`filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}]}`, `page_size=100`

Group by: 1. `categories[0]` (platform-assigned) — primary grouping 2. `metadata.type` (agent-assigned) — secondary if no categories 3. `created_at` date — for age analysis

**Category normalization:** Merge `auto_capture` and `uncategorized` into a single `uncategorized` row. These are memories where the platform didn't assign a meaningful content category. Do NOT show `auto_capture` as its own row in the table.

**Session stats (local only):** Session stats come from the local stats file read in Step 1. Do NOT query the API with `run_id` or `metadata.session_id` filters — these return unreliable results because memories are stored without `run_id` and metadata filters on `session_id` are inconsistent.

The local stats file tracks adds and searches for the current session accurately.

Also run a `search_memories` MCP tool call with `query="project"`, `filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}]}`, `top_k=1` to measure round-trip latency. Note the time before and after the MCP call — do NOT attempt raw HTTP calls to the API.

Step 3: Display

Print a minimal dashboard. No ASCII bar charts — use a clean table layout:

## mem0 stats

**Session** (<session_id, first 12 chars>) — 3 written, 5 searches, categories: decision, convention

**Project: my-project** — 55 memories, API: 84ms

| Category             | Count |
|----------------------|-------|
| decision             |    24 |
| convention           |    15 |
| anti_pattern         |     6 |
| task_learning        |     5 |
| user_preference      |     3 |
| session_state        |     2 |

**Age** — oldest: 2026-02-15, newest: 2026-05-23
  < 7 days: 5 · 7–30d: 12 · 30–90d: 10 · > 90d: 8

**Identity** — user: kartik · project: my-project · branch: main

**Display rules:**

  • Category table: sort by count descending, omit categories with 0 memories
  • Age: single line with dot-separated buckets, computed from `created_at`
  • Session line: skip if no session data available
  • If only 1-2 total memories, skip the category table — just show the count
  • Keep everything compact — no decorative borders or filler

Weekly digest mode

When invoked with `--weekly` (e.g., `/mem0:stats --weekly`), append a weekly activity digest after the standard stats dashboard:

W1: Fetch recent memories

Call `search_memories` in parallel with time-scoped queries: 1. `query="decisions made this week"`, `filters={"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}, {"created_at": {"gte": "<7 days ago YYYY-MM-DD>"}}]}`, `top_k=20` 2. `query="bugs errors fixes"`, same time filter, `top_k=20` 3. `query="patterns conventions learnings"`, same time filter, `top_k=20`

W2: Analyze

Merge by ID. Group into "New this week" by `categories[0]` or `metadata.type`. Calculate: memories added last 7 days, most active categories, most active day.

W3: Display

Append after the standard stats:

### This week (May 16 – May 23)

+12 memories — most active: Wednesday (5)

| Category      | New |
|---------------|-----|
| decision      |   5 |
| task_learning |   4 |
| bug_fix       |   3 |

**Highlights**
- <2-3 sentence summary of most important decisions/learnings this week>

W4: Write digest file

Write to `~/.mem0/weekly-digest.md` (overwrite). Append one-line to `~/.mem0/digest-history.log`:

<YYYY-MM-DD> | <project_id> | +<new_count> memories | top: <top_category>

W5: Empty state

If no new memories in 7 days:

No new memories in the past week. Total: <N> memories in <project_id>.
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Mem0 ("mem-zero") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions.

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Repo: mem0ai/mem0

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